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Projecting named entity recognizers without annotated or parallel corpora

Jue Hou
Department of Computer Science, University of Helsinki, Finland

Maximilian W. Koppatz
Department of Computer Science, University of Helsinki, Finland

José María Hoya Quecedo
Department of Computer Science, University of Helsinki, Finland

Roman Yangarber
Department of Computer Science, University of Helsinki, Finland

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Ingår i: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Linköping Electronic Conference Proceedings 167:24, s. 232--241

NEALT Proceedings Series 42:24, s. 232--241

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Publicerad: 2019-10-02

ISBN: 978-91-7929-995-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpora, such as Finnish. We propose an approach to create a named entity recognizer with no annotated or parallel documents, by leveraging strong NER models that exist for English. We automatically gather a large amount of {\em chronologically matched} data in two languages, then project named entity annotations from the English documents onto the Finnish ones, by resolving the matches with limited linguistic rules. We use this ``artificially’’ annotated data to train a BiLSTM-CRF model. Our results show that this method can produce annotated instances with high precision, and the resulting model achieves state-of-the-art performance.

Nyckelord

Automatic data annotation Named Entity Recognition Neural Network

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